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   "source": [
    "# ROMS Ocean Model Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Regional Ocean Modeling System ([ROMS](http://myroms.org)) is an open source hydrodynamic model that is used for simulating currents and water properties in coastal and estuarine regions. ROMS is one of a few standard ocean models, and it has an active user community.\n",
    "\n",
    "ROMS uses a regular C-Grid in the horizontal, similar to other structured grid ocean and atmospheric models, and a stretched vertical coordinate (see [the ROMS documentation](https://www.myroms.org/wiki/Vertical_S-coordinate) for more details). Both of these require special treatment when using `xarray` to analyze ROMS ocean model output. This example notebook shows how to create a lazily evaluated vertical coordinate, and make some basic plots. The `xgcm` package is required to do analysis that is aware of the horizontal C-Grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a sample ROMS file. This is a subset of a full model available at \n",
    "\n",
    "    http://barataria.tamu.edu/thredds/catalog.html?dataset=txla_hindcast_agg\n",
    "    \n",
    "The subsetting was done using the following command on one of the output files:\n",
    "\n",
    "    #open dataset\n",
    "    ds = xr.open_dataset('/d2/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc')\n",
    "    \n",
    "    # Turn on chunking to activate dask and parallelize read/write.\n",
    "    ds = ds.chunk({'ocean_time': 1})\n",
    "    \n",
    "    # Pick out some of the variables that will be included as coordinates\n",
    "    ds = ds.set_coords(['Cs_r', 'Cs_w', 'hc', 'h', 'Vtransform'])\n",
    "    \n",
    "    # Select a a subset of variables. Salt will be visualized, zeta is used to \n",
    "    # calculate the vertical coordinate\n",
    "    variables = ['salt', 'zeta']\n",
    "    ds[variables].isel(ocean_time=slice(47, None, 7*24), \n",
    "                       xi_rho=slice(300, None)).to_netcdf('ROMS_example.nc', mode='w')\n",
    "\n",
    "So, the `ROMS_example.nc` file contains a subset of the grid, one 3D variable, and two time steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in ROMS dataset as an xarray object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the file\n",
    "ds = xr.tutorial.open_dataset(\"ROMS_example.nc\", chunks={\"ocean_time\": 1})\n",
    "\n",
    "# This is a way to turn on chunking and lazy evaluation. Opening with mfdataset, or\n",
    "# setting the chunking in the open_dataset would also achieve this.\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a lazilly calculated vertical coordinates\n",
    "\n",
    "Write equations to calculate the vertical coordinate. These will be only evaluated when data is requested. Information about the ROMS vertical coordinate can be found (here)[https://www.myroms.org/wiki/Vertical_S-coordinate]\n",
    "\n",
    "In short, for `Vtransform==2` as used in this example, \n",
    "\n",
    "$Z_0 = (h_c \\, S + h \\,C) / (h_c + h)$\n",
    "\n",
    "$z = Z_0 (\\zeta + h) + \\zeta$\n",
    "\n",
    "where the variables are defined as in the link above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if ds.Vtransform == 1:\n",
    "    Zo_rho = ds.hc * (ds.s_rho - ds.Cs_r) + ds.Cs_r * ds.h\n",
    "    z_rho = Zo_rho + ds.zeta * (1 + Zo_rho / ds.h)\n",
    "elif ds.Vtransform == 2:\n",
    "    Zo_rho = (ds.hc * ds.s_rho + ds.Cs_r * ds.h) / (ds.hc + ds.h)\n",
    "    z_rho = ds.zeta + (ds.zeta + ds.h) * Zo_rho\n",
    "\n",
    "ds.coords[\"z_rho\"] = z_rho.transpose()  # needing transpose seems to be an xarray bug\n",
    "ds.salt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A naive vertical slice\n",
    "\n",
    "Creating a slice using the s-coordinate as the vertical dimension is typically not very informative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ds.salt.isel(xi_rho=50, ocean_time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can feed coordinate information to the plot method to give a more informative cross-section that uses the depths. Note that we did not need to slice the depth or longitude information separately, this was done automatically as the variable was sliced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "section = ds.salt.isel(xi_rho=50, eta_rho=slice(0, 167), ocean_time=0)\n",
    "section.plot(x=\"lon_rho\", y=\"z_rho\", figsize=(15, 6), clim=(25, 35))\n",
    "plt.ylim([-100, 1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A plan view\n",
    "\n",
    "Now make a naive plan view, without any projection information, just using lon/lat as x/y. This looks OK, but will appear compressed because lon and lat do not have an aspect constrained by the projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.salt.isel(s_rho=-1, ocean_time=0).plot(x=\"lon_rho\", y=\"lat_rho\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's use a projection to make it nicer, and add a coast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj = ccrs.LambertConformal(central_longitude=-92, central_latitude=29)\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = plt.axes(projection=proj)\n",
    "ds.salt.isel(s_rho=-1, ocean_time=0).plot(\n",
    "    x=\"lon_rho\", y=\"lat_rho\", transform=ccrs.PlateCarree()\n",
    ")\n",
    "\n",
    "coast_10m = cfeature.NaturalEarthFeature(\n",
    "    \"physical\", \"land\", \"10m\", edgecolor=\"k\", facecolor=\"0.8\"\n",
    ")\n",
    "ax.add_feature(coast_10m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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